OpenRaiser/Intern-Atlas huggingface dataset:
Deep learning approaches in food recognition
Venue: arXiv cs.CV 2020
Year: 2020

Automatic image-based food recognition is a particularly challenging task. Traditional image analysis approaches have achieved low classification accuracy in the past, whereas deep learning approaches enabled the identification of food types and their ingredients. The contents of food dishes are typically deformable objects, usually including complex semantics, which makes the task of defining their structure very difficult. Deep learning methods have already shown very promising results in such challenges, so this chapter focuses on the presentation of some popular approaches and techniques applied in image-based food recognition. The three main lines of solutions, namely the design from scratch, the transfer learning and the platform-based approaches, are outlined, particularly for the task at hand, and are tested and compared to reveal the inherent strengths and weaknesses. The chapter is complemented with basic background material, a section devoted to the relevant datasets that are crucial in light of the empirical approaches adopted, and some concluding remarks that underline the future directions.
Man in the middle and current injection attacks against the KLJN key exchanger compromised by DC sources
Venue: arXiv cs.CR 2020
Year: 2020

This study addresses a new question regarding the security of the Kirchhoff-Law-Johnson-Noise (KLJN) scheme compromised by DC sources at Alice and Bob: What is the impact of these parasitic sources on active attacks, such as the man-in-the-middle (MITM) attack, or the current injection attack? The surprising answer is that the parasitic DC sources actually increase the security of the system because, in the case of the MITM attack, they make easier to uncover the eavesdropping. In some of the cases Eve can fix this deficiency but then the problem gets reduced to the original MITM attack to which the KLJN scheme is immune, it is already as proven earlier
Error-Corrected Margin-Based Deep Cross-Modal Hashing for Facial Image Retrieval
Venue: arXiv cs.CV 2020
Year: 2020

Cross-modal hashing facilitates mapping of heterogeneous multimedia data into a common Hamming space, which can beutilized for fast and flexible retrieval across different modalities. In this paper, we propose a novel cross-modal hashingarchitecture-deep neural decoder cross-modal hashing (DNDCMH), which uses a binary vector specifying the presence of certainfacial attributes as an input query to retrieve relevant face images from a database. The DNDCMH network consists of two separatecomponents: an attribute-based deep cross-modal hashing (ADCMH) module, which uses a margin (m)-based loss function toefficiently learn compact binary codes to preserve similarity between modalities in the Hamming space, and a neural error correctingdecoder (NECD), which is an error correcting decoder implemented with a neural network. The goal of NECD network in DNDCMH isto error correct the hash codes generated by ADCMH to improve the retrieval efficiency. The NECD network is trained such that it hasan error correcting capability greater than or equal to the margin (m) of the margin-based loss function. This results in NECD cancorrect the corrupted hash codes generated by ADCMH up to the Hamming distance of m. We have evaluated and comparedDNDCMH with state-of-the-art cross-modal hashing methods on standard datasets to demonstrate the superiority of our method.
Efficient Context and Schema Fusion Networks for Multi-Domain Dialogue State Tracking
Venue: arXiv cs.CL 2020
Year: 2020

Dialogue state tracking (DST) aims at estimating the current dialogue state given all the preceding conversation. For multi-domain DST, the data sparsity problem is a major obstacle due to increased numbers of state candidates and dialogue lengths. To encode the dialogue context efficiently, we utilize the previous dialogue state (predicted) and the current dialogue utterance as the input for DST. To consider relations among different domain-slots, the schema graph involving prior knowledge is exploited. In this paper, a novel context and schema fusion network is proposed to encode the dialogue context and schema graph by using internal and external attention mechanisms. Experiment results show that our approach can obtain new state-of-the-art performance of the open-vocabulary DST on both MultiWOZ 2.0 and MultiWOZ 2.1 benchmarks.
Markov Decision Process Based Design of SWIPT Systems: Non-linear EH Circuits, Memory, and Impedance Mismatch
Venue: arXiv cs.IT 2020
Year: 2020

In this paper, we study simultaneous wireless information and power transfer (SWIPT) systems employing practical non-linear energy harvester (EH) circuits. Since the voltage across the reactive elements of realistic EH circuits cannot drop or rise instantaneously, EHs have memory which we model with a Markov decision process (MDP). Moreover, since an analytical model that accurately models all non-linear effects and the unavoidable impedance mismatch of EHs is not tractable, we propose a learning based model for the EH circuit. We optimize the input signal distribution for maximization of the harvested power under a constraint on the minimum mutual information between transmitter (TX) and information receiver (IR). We distinguish the cases where the MDP state is known and not known at TX and IR. When the MDP state is known, the formulated optimization problem for the harvested power is convex. In contrast, if TX and IR do not know the MDP state, the resulting optimization problem is non-convex and solved via alternating optimization, which is shown to yield a limit point of the problem. Our simulation results reveal that the rate-power region of the considered SWIPT system depends on the symbol duration, the EH input power level, the EH impedance mismatch, and the type of EH circuit. In particular, a shorter symbol duration enables higher bit rates at the expense of a significant decrease in the average harvested power. Furthermore, whereas half-wave rectifiers outperform full-wave rectifiers in the low and medium input power regimes, full-wave rectifiers are preferable if the input power at the EH is high.
Beer Organoleptic Optimisation: Utilising Swarm Intelligence and Evolutionary Computation Methods
Venue: arXiv cs.NE 2020
Year: 2020

Customisation in food properties is a challenging task involving optimisation of the production process with the demand to support computational creativity which is geared towards ensuring the presence of alternatives. This paper addresses the personalisation of beer properties in the specific case of craft beers where the production process is more flexible. We investigate the problem by using three swarm intelligence and evolutionary computation techniques that enable brewers to map physico-chemical properties to target organoleptic properties to design a specific brew. While there are several tools, using the original mathematical and chemistry formulas, or machine learning models that deal with the process of determining beer properties based on the pre-determined quantities of ingredients, the next step is to investigate an automated quantitative ingredient selection approach. The process is illustrated by a number of experiments designing craft beers where the results are investigated by "cloning" popular commercial brands based on their known properties. Algorithms performance is evaluated using accuracy, efficiency, reliability, population-diversity, iteration-based improvements and solution diversity. The proposed approach allows for the discovery of new recipes, personalisation and alternative high-fidelity reproduction of existing ones.
QuantNet: Transferring Learning Across Systematic Trading Strategies
Venue: arXiv cs.LG 2020
Year: 2020

Systematic financial trading strategies account for over 80% of trade volume in equities and a large chunk of the foreign exchange market. In spite of the availability of data from multiple markets, current approaches in trading rely mainly on learning trading strategies per individual market. In this paper, we take a step towards developing fully end-to-end global trading strategies that leverage systematic trends to produce superior market-specific trading strategies. We introduce QuantNet: an architecture that learns market-agnostic trends and use these to learn superior market-specific trading strategies. Each market-specific model is composed of an encoder-decoder pair. The encoder transforms market-specific data into an abstract latent representation that is processed by a global model shared by all markets, while the decoder learns a market-specific trading strategy based on both local and global information from the market-specific encoder and the global model. QuantNet uses recent advances in transfer and meta-learning, where market-specific parameters are free to specialize on the problem at hand, whilst market-agnostic parameters are driven to capture signals from all markets. By integrating over idiosyncratic market data we can learn general transferable dynamics, avoiding the problem of overfitting to produce strategies with superior returns. We evaluate QuantNet on historical data across 3103 assets in 58 global equity markets. Against the top performing baseline, QuantNet yielded 51% higher Sharpe and 69% Calmar ratios. In addition we show the benefits of our approach over the non-transfer learning variant, with improvements of 15% and 41% in Sharpe and Calmar ratios. Code available in appendix.
BreastScreening: On the Use of Multi-Modality in Medical Imaging Diagnosis
Venue: arXiv cs.HC 2020
Year: 2020

This paper describes the field research, design and comparative deployment of a multimodal medical imaging user interface for breast screening. The main contributions described here are threefold: 1) The design of an advanced visual interface for multimodal diagnosis of breast cancer (BreastScreening); 2) Insights from the field comparison of single vs multimodality screening of breast cancer diagnosis with 31 clinicians and 566 images, and 3) The visualization of the two main types of breast lesions in the following image modalities: (i) MammoGraphy (MG) in both Craniocaudal (CC) and Mediolateral oblique (MLO) views; (ii) UltraSound (US); and (iii) Magnetic Resonance Imaging (MRI). We summarize our work with recommendations from the radiologists for guiding the future design of medical imaging interfaces.
A categorical approach to secure compilation
Venue: arXiv cs.PL 2020
Year: 2020

We introduce a novel approach to secure compilation based on maps of distributive laws. We demonstrate through four examples that the coherence criterion for maps of distributive laws can potentially be a viable alternative for compiler security instead of full abstraction, which is the preservation and reflection of contextual equivalence. To that end, we also make use of the well-behavedness properties of distributive laws to construct a categorical argument for the contextual connotations of bisimilarity.
Two Results on Layered Pathwidth and Linear Layouts
Venue: arXiv cs.DM 2020
Year: 2020

Layered pathwidth is a new graph parameter studied by Bannister et al (2015). In this paper we present two new results relating layered pathwidth to two types of linear layouts. Our first result shows that, for any graph $G$, the stack number of $G$ is at most four times the layered pathwidth of $G$. Our second result shows that any graph $G$ with track number at most three has layered pathwidth at most four. The first result complements a result of Dujmović and Frati (2018) relating layered treewidth and stack number. The second result solves an open problem posed by Bannister et al (2015).
Multimodal Image Synthesis with Conditional Implicit Maximum Likelihood Estimation
Venue: arXiv cs.CV 2020
Year: 2020

Many tasks in computer vision and graphics fall within the framework of conditional image synthesis. In recent years, generative adversarial nets (GANs) have delivered impressive advances in quality of synthesized images. However, it remains a challenge to generate both diverse and plausible images for the same input, due to the problem of mode collapse. In this paper, we develop a new generic multimodal conditional image synthesis method based on Implicit Maximum Likelihood Estimation (IMLE) and demonstrate improved multimodal image synthesis performance on two tasks, single image super-resolution and image synthesis from scene layouts. We make our implementation publicly available.
New Datasets and a Benchmark of Document Network Embedding Methods for Scientific Expert Finding
Venue: arXiv cs.IR 2020
Year: 2020

The scientific literature is growing faster than ever. Finding an expert in a particular scientific domain has never been as hard as today because of the increasing amount of publications and because of the ever growing diversity of expertise fields. To tackle this challenge, automatic expert finding algorithms rely on the vast scientific heterogeneous network to match textual queries with potential expert candidates. In this direction, document network embedding methods seem to be an ideal choice for building representations of the scientific literature. Citation and authorship links contain major complementary information to the textual content of the publications. In this paper, we propose a benchmark for expert finding in document networks by leveraging data extracted from a scientific citation network and three scientific question & answer websites. We compare the performances of several algorithms on these different sources of data and further study the applicability of embedding methods on an expert finding task.
Towards Faithfully Interpretable NLP Systems: How should we define and evaluate faithfulness?
Venue: arXiv cs.CL 2020
Year: 2020

With the growing popularity of deep-learning based NLP models, comes a need for interpretable systems. But what is interpretability, and what constitutes a high-quality interpretation? In this opinion piece we reflect on the current state of interpretability evaluation research. We call for more clearly differentiating between different desired criteria an interpretation should satisfy, and focus on the faithfulness criteria. We survey the literature with respect to faithfulness evaluation, and arrange the current approaches around three assumptions, providing an explicit form to how faithfulness is "defined" by the community. We provide concrete guidelines on how evaluation of interpretation methods should and should not be conducted. Finally, we claim that the current binary definition for faithfulness sets a potentially unrealistic bar for being considered faithful. We call for discarding the binary notion of faithfulness in favor of a more graded one, which we believe will be of greater practical utility.
Cutting cycles of rods in space is FPT
Venue: arXiv cs.CG 2020
Year: 2020

In this short note, we show that cutting cycles of rods is fixed-parameter tractable by reducing the problem to computing a feedback vertex set in a mixed graph.
Mobile-Based Deep Learning Models for Banana Diseases Detection
Venue: arXiv cs.CV 2020
Year: 2020

Smallholder farmers in Tanzania are challenged on the lack of tools for early detection of banana diseases. This study aimed at developing a mobile application for early detection of Fusarium wilt race 1 and black Sigatoka banana diseases using deep learning. We used a dataset of 3000 banana leaves images. We pre-trained our model on Resnet152 and Inceptionv3 Convolution Neural Network architectures. The Resnet152 achieved an accuracy of 99.2% and Inceptionv3 an accuracy of 95.41%. On deployment using Android mobile phones, we chose Inceptionv3 since it has lower memory requirements compared to Resnet152. The mobile application on real environment detected the two diseases with a confidence level of 99% of the captured leaf area. This result indicates the potential in improving the yield of bananas by smallholder farmers using a tool for early detection of diseases.
DashCam Pay: A System for In-vehicle Payments Using Face and Voice
Venue: arXiv cs.CR 2020
Year: 2020

We present our ongoing work on developing a system, called DashCam Pay, that enables in-vehicle payments in a seamless and secure manner using face and voice biometrics. A plug-and-play device (dashcam) mounted in the vehicle is used to capture face images and voice commands of passengers. Privacy-preserving biometric comparison techniques are used to compare the biometric data captured by the dashcam with the biometric data enrolled on the users' mobile devices over a wireless interface (e.g., Bluetooth or Wi-Fi Direct) to determine the payer. Once the payer is identified, payment is conducted using the enrolled payment credential on the mobile device of the payer. We conduct preliminary analysis on data collected using a commercially available dashcam to show the feasibility of building the proposed system. A prototype of the proposed system is also developed in Android. DashCam Pay can be integrated as a software solution by dashcam or vehicle manufacturers to enable open loop in-vehicle payments.
Adaptive Transformers in RL
Venue: arXiv cs.LG 2020
Year: 2020

Recent developments in Transformers have opened new interesting areas of research in partially observable reinforcement learning tasks. Results from late 2019 showed that Transformers are able to outperform LSTMs on both memory intense and reactive tasks. In this work we first partially replicate the results shown in Stabilizing Transformers in RL on both reactive and memory based environments. We then show performance improvement coupled with reduced computation when adding adaptive attention span to this Stable Transformer on a challenging DMLab30 environment. The code for all our experiments and models is available at https://github.com/jerrodparker20/adaptive-transformers-in-rl.
MirrorNet: A Deep Bayesian Approach to Reflective 2D Pose Estimation from Human Images
Venue: arXiv cs.CV 2020
Year: 2020

This paper proposes a statistical approach to 2D pose estimation from human images. The main problems with the standard supervised approach, which is based on a deep recognition (image-to-pose) model, are that it often yields anatomically implausible poses, and its performance is limited by the amount of paired data. To solve these problems, we propose a semi-supervised method that can make effective use of images with and without pose annotations. Specifically, we formulate a hierarchical generative model of poses and images by integrating a deep generative model of poses from pose features with that of images from poses and image features. We then introduce a deep recognition model that infers poses from images. Given images as observed data, these models can be trained jointly in a hierarchical variational autoencoding (image-to-pose-to-feature-to-pose-to-image) manner. The results of experiments show that the proposed reflective architecture makes estimated poses anatomically plausible, and the performance of pose estimation improved by integrating the recognition and generative models and also by feeding non-annotated images.
A Public Safety Framework for Immersive Aerial Monitoring through 5G Commercial Network
Venue: arXiv cs.NI 2020
Year: 2020

Are 5G connection and UAVs merely parts of an extravagant and luxurious world, or are they essential parts of a practical world in a way we have yet to see? To aid in a direction to address the issue, we provide a practical framework for immersive aerial monitoring for public safety. Because the framework is built on top of actual realizations and implementations designed to fulfill specific use cases, high level of practicality is ensured by nature. We first investigate 5G network performance on UAVs by isolating performance for different aspects of expected flight missions. Finally, the novel aerial monitoring scheme that we introduce relies on the recent advances brought by 5G networks and mitigates the inherent limitations of 5G network that we investigate in this paper.
Monte-Carlo Siamese Policy on Actor for Satellite Image Super Resolution
Venue: arXiv cs.CV 2020
Year: 2020

In the past few years supervised and adversarial learning have been widely adopted in various complex computer vision tasks. It seems natural to wonder whether another branch of artificial intelligence, commonly known as Reinforcement Learning (RL) can benefit such complex vision tasks. In this study, we explore the plausible usage of RL in super resolution of remote sensing imagery. Guided by recent advances in super resolution, we propose a theoretical framework that leverages the benefits of supervised and reinforcement learning. We argue that a straightforward implementation of RL is not adequate to address ill-posed super resolution as the action variables are not fully known. To tackle this issue, we propose to parameterize action variables by matrices, and train our policy network using Monte-Carlo sampling. We study the implications of parametric action space in a model-free environment from theoretical and empirical perspective. Furthermore, we analyze the quantitative and qualitative results on both remote sensing and non-remote sensing datasets. Based on our experiments, we report considerable improvement over state-of-the-art methods by encapsulating supervised models in a reinforcement learning framework.
How to Best Predict the Daily Number of New Infections of Covid-19
Venue: arXiv cs.SI 2020
Year: 2020

Knowledge about the daily number of new infections of Covid-19 is important because it is the basis for political decisions resulting in lockdowns and urgent health care measures. We use Germany as an example to illustrate shortcomings of official numbers, which are, at least in Germany, disclosed only with several days of delay and severely underreported on weekends (more than 40%). These shortcomings outline an urgent need for alternative data sources. The other widely cited source provided by the Center for Systems Science and Engineering at Johns Hopkins University (JHU) also deviates for Germany on average by 79% from the official numbers. We argue that Google Search and Twitter data should complement official numbers. They predict even better than the original values from Johns Hopkins University and do so several days ahead. These two data sources could also be used in parts of the world where official numbers do not exist or are perceived to be unreliable.
Adversary Helps: Gradient-based Device-Free Domain-Independent Gesture Recognition
Venue: arXiv cs.CV 2020
Year: 2020

Wireless signal-based gesture recognition has promoted the developments of VR game, smart home, etc. However, traditional approaches suffer from the influence of the domain gap. Low recognition accuracy occurs when the recognition model is trained in one domain but is used in another domain. Though some solutions, such as adversarial learning, transfer learning and body-coordinate velocity profile, have been proposed to achieve cross-domain recognition, these solutions more or less have flaws. In this paper, we define the concept of domain gap and then propose a more promising solution, namely DI, to eliminate domain gap and further achieve domain-independent gesture recognition. DI leverages the sign map of the gradient map as the domain gap eliminator to improve the recognition accuracy. We conduct experiments with ten domains and ten gestures. The experiment results show that DI can achieve the recognition accuracies of 87.13%, 90.12% and 94.45% on KNN, SVM and CNN, which outperforms existing solutions.